Adaptive exponential integrate-and-fire model as an effective description of neuronal activity.

By Romain Brette, Wulfram Gerstner. Journal of Neurophysiology, 2005

1,322 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Electrical and Electronic Engineering, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/d706e8529328c903ac2a69d9df1b9e547a5235dd

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Citations in the corpus

Enabling Spike-Based Backpropagation for Training Deep Neural Network Architectures (2019, background): “…SNN, which is often regarded as the 3rd generation neural network (Maass, 1997), are inspired by the biological neuronal mechanisms (Hodgkin and Huxley, 1952; Dayan and Abbott, 2001; Izhikevich, 2003; Brette and Gerstner, 2005) that can efficiently process discrete spatio-temporal events (spikes).”
Neuromorphic Electronic Circuits for Building Autonomous Cognitive Systems (2014, background): “Consequently, in order to interact with the environment and process real-world sensory signals efficiently, neuromorphic behaving systems must use circuits that have biologically plausible time constants (i.e., of the order of tens of milliseconds).”
Connectivity reflects coding: a model of voltage-based STDP with homeostasis (2010, methodology): “As a model for neuronal voltage, we chose the adaptive exponential integrate-and-fire (AdEx) mode”
Stochastic Integrate and Fire Models: a review on mathematical methods and their applications (2011, neutral): “for example [13], [26], [16]).”
Computing the Local Field Potential (LFP) from Integrate-and-Fire Network Models (2015, background): “The equations describing the single LIF neuron are simple and can be easily adapted to generate complex dynamics [3,4].”
A reconfigurable on-line learning spiking neuromorphic processor comprising 256 neurons and 128K synapses (2015, methodology): “The silicon neurons contain circuits that implement a model of the adaptive exponential Integrate-and-Fire (I&F) neuron (Brette and Gerstner, 2005), post-synaptic learning circuits used to implement the spike-based weight-update/plasticity mechanism in the array of long-term plasticity synapses,…”
Tag-Trigger-Consolidation: A Model of Early and Late Long-Term-Potentiation and Depression (2008, methodology): “Parameters for the neuron model are as in [42] and are kept fixed for all simulations presented in this paper.”
Dendrites of dentate gyrus granule cells contribute to pattern separation by controlling sparsity (2016, methodology): “Specifically, an adaptive exponential I&F model (aEIF) (Brette and Gerstner, 2005) was used to model MCs, BCs, and HIPP cells.”
A 0.086-mm$^2$ 12.7-pJ/SOP 64k-Synapse 256-Neuron Online-Learning Digital Spiking Neuromorphic Processor in 28-nm CMOS (2018, methodology): “In contrast, implementing biophysically-accurate models (e.g., Hodgkin-Huxley [38], Izhikevich [39] or adaptive-exponential [40]) requires digital SNNs to solve coupled non-linear differential equations and to update all neuron states at each integration timestep.”
Synaptic Plasticity in Neural Networks Needs Homeostasis with a Fast Rate Detector (2013, background): “The timescales involved in firing rate adaptation are typically short (on the order of 100 ms) and their effect therefore negligible at the low firing rates of background activity [61,62].”
Spiking Neural Networks and Their Applications: A Review (2022, background): “…neuron can be considered to be an adaptive quadratic integrate-and-ﬁre model, whereas the adaptive exponential integrate-and-ﬁre (AdEx) model [41] has an exponential voltage dependence, coupled with a slow variable, which models threshold adaptation as follows: with reset conditions where w…”
Simulation of networks of spiking neurons: A review of tools and strategies (2006, neutral): “However, there are still efforts to be made to design suitable algorithms for more complex models [for example the two-variable IF models of Izhikevich (2003) and Brette and Gerstner (2005)], or to develop more realistic models that are suitable for event-driven simulation.”
Memory and Information Processing in Neuromorphic Systems (2015, background): “7 implement a model of the adaptive exponential I&F neuron [136] that has been shown to be able to accurately reproduce electrophysiological recordings of real neurons [137], [138].”
Firing patterns in the adaptive exponential integrate-and-fire model (2008, methodology): “Optimization methods Brette and Gerstner (2005) have proposed an experimental protocol to determine the parameters of the model.”